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Data Preparation and Overview

Setup

Load Packages

#Load Packages

suppressPackageStartupMessages({
library(BiocParallel)
library(ggplot2) 
library(dplyr)
library(DropletUtils)
library(scater)
})

Set parameter

set.seed(100)
bpp <- BiocParallel::MulticoreParam(parallel::detectCores()-1)
path <- here::here()

Load Data

Load Cellbender output matrices

Load in the filtered feature barcode matrices from Cellbender of each sample and save them as sce object.

## RA DATASET
rawdata_folder <- paste0(path,"/data/cellbender_data_h5/")


#Get file names
filenames <- list.files(rawdata_folder ,recursive = F, full.names = F,pattern = "\\_filtered.h5$")
filepaths <- paste(rawdata_folder,filenames,sep = "")

#Load data as sce object
sce<-read10xCounts(samples = filepaths, sample.names = filenames,col.names=T,type="HDF5")


colData(sce)$Sample = sub("\\_w_introns.*", "", sub("cellbender_output_", "", as.character(colData(sce)$Sample)))


sce
class: SingleCellExperiment 
dim: 36601 119652 
metadata(1): Samples
assays(1): counts
rownames(36601): ENSG00000243485 ENSG00000237613 ... ENSG00000278817
  ENSG00000277196
rowData names(3): ID Symbol Type
colnames(119652): 1_AAGTACCGTCTCTCAC-1 1_GACTCTCAGGTAACTA-1 ...
  17_AGGTGTTGTGGCATCC-1 17_GCCAGCACACAAGTGG-1
colData names(2): Sample Barcode
reducedDimNames(0):
mainExpName: NULL
altExpNames(0):

Load and add Metadata

#Metadata path
metadata_folder <- paste0(path,"/data/metadata/Metadata_Master.csv")

#Load metadata
metadata_df <-read.csv(metadata_folder, header=TRUE, sep=",")
metadata_df
#Add metadata to sce
colData(sce) <- dplyr::left_join(as.data.frame(colData(sce)),
                                   metadata_df, 
                                   by= c("Sample" = "orig.ident"),
                                   suffix=c(".x",".y")) %>% 
      #dplyr::select(Sample, Barcode, Diagnosis, sex, Age, Joint.Location, protocol, Pathotype,Krenn total score,) %>% 
      dplyr::select(-one_of("Comments")) %>% #select all except
      DataFrame(row.names=colnames(sce))


#make row and col names unique
colnames(sce) <- paste0(sce$Sample, ".", sce$Barcode)
rownames(sce) <- paste0(rowData(sce)$ID, ".", rowData(sce)$Symbol)

Explore dataset

Dimensions of the count matrix

#Dimensions of count matrix
dim(sce)
[1]  36601 119652
#Feautures/row data
data.frame(colData(sce))
#Droplet details / row data
data.frame(rowData(sce))

Exploratory plots

Histogramm with number of cells

Show the number of cells detected in each sample or joint location before filtering

Per Sample

#Histogramm with number of cells per sample
ggplot(colData(sce), aes(x=Sample))+geom_bar()+ coord_flip()+ ggtitle("Number of cells per sample")

Version Author Date
bc77174 sarloet 2024-04-03
d291fd3 sarloet 2024-04-02 
data.frame(as.list(table(colData(sce)$Sample)))

Per Joint Location

#Histogramm with number of cells per sample
ggplot(colData(sce), aes(x=Joint.Location))+geom_bar()+ coord_flip()+ ggtitle("Number of cells per sample")

Version Author Date
bc77174 sarloet 2024-04-03 
data.frame(as.list(table(colData(sce)$Joint.Location)))

Plot number of genes detected per cell

This plot shows cell counts per sample / count occurrence

#Number of genes detected per cell
#Total UMI for a gene versus the number of times detected
genesPerCell <- colSums(counts(sce) > 0)
plot(density(genesPerCell), xlab="Genes per cell", main="Number of genes detected per cell")

Version Author Date
bc77174 sarloet 2024-04-03
d291fd3 sarloet 2024-04-02

Plot transcript capture efficiency

This plot gives an idea about the sequencing depth and if the sequencing has reached saturation or not. Plotted is the total gene count across all cells (x-axis) vs Proportion of cells the gene is detected in (y-axis) where each dot represents a gene.

#transcript_capture_efficiency
#Total UMI for a gene versus the number of times detected
tmpCounts <- counts(sce)

plot(rowSums(tmpCounts),
     rowMeans(tmpCounts > 0),
     log = "x",
     xlab="total number of UMIs",
     ylab="proportion of cells expressing the gene",
     main="Total UMI for a gene vs times detected")

Version Author Date
bc77174 sarloet 2024-04-03
d291fd3 sarloet 2024-04-02

Plot top 25 genes in the dataset

The most strongly and commonly expressed genes expected among all cells are ribosomal and mitochondrial.

#Distribution of counts for the top 20 genes across cells

#rel_expression <- t( t(tmpCounts) / colSums(tmpCounts)) * 100
#rownames(rel_expression) <- rowData(sce)$Symbol
#most_expressed <- sort(rowSums( rel_expression ), decreasing = T)[20:1] ##
#plot_data <- as.matrix(t(rel_expression[names(most_expressed),]))

#boxplot(plot_data, cex=0.1, las=1, xlab="% total count per cell", horizontal=TRUE, main="Distribution of counts for the top 20 genes before filtering")

#rm(tmpCounts)

plotHighestExprs(sce,n = 25)

Save the dataset

saveRDS(sce, file =paste0(path,'/output/00_sce_DataPreparation.rds'))

sessionInfo()
R version 4.3.1 (2023-06-16)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 22.04.3 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

time zone: Etc/UTC
tzcode source: system (glibc)

attached base packages:
[1] stats4    stats     graphics  grDevices utils     datasets  methods  
[8] base     

other attached packages:
 [1] scater_1.28.0               scuttle_1.10.3             
 [3] DropletUtils_1.20.0         SingleCellExperiment_1.22.0
 [5] SummarizedExperiment_1.30.2 Biobase_2.60.0             
 [7] GenomicRanges_1.52.1        GenomeInfoDb_1.36.4        
 [9] IRanges_2.34.1              S4Vectors_0.38.2           
[11] BiocGenerics_0.46.0         MatrixGenerics_1.12.3      
[13] matrixStats_1.2.0           dplyr_1.1.4                
[15] ggplot2_3.5.0               BiocParallel_1.34.2        
[17] workflowr_1.7.1            

loaded via a namespace (and not attached):
 [1] bitops_1.0-7              gridExtra_2.3            
 [3] rlang_1.1.3               magrittr_2.0.3           
 [5] git2r_0.33.0              compiler_4.3.1           
 [7] getPass_0.2-4             DelayedMatrixStats_1.22.6
 [9] callr_3.7.3               vctrs_0.6.5              
[11] stringr_1.5.1             pkgconfig_2.0.3          
[13] crayon_1.5.2              fastmap_1.1.1            
[15] XVector_0.40.0            labeling_0.4.3           
[17] utf8_1.2.4                promises_1.2.1           
[19] rmarkdown_2.26            ps_1.7.5                 
[21] ggbeeswarm_0.7.2          xfun_0.43                
[23] zlibbioc_1.46.0           cachem_1.0.8             
[25] beachmat_2.16.0           jsonlite_1.8.8           
[27] highr_0.10                later_1.3.2              
[29] rhdf5filters_1.12.1       DelayedArray_0.26.7      
[31] Rhdf5lib_1.22.0           irlba_2.3.5.1            
[33] parallel_4.3.1            R6_2.5.1                 
[35] bslib_0.5.1               stringi_1.8.3            
[37] limma_3.56.2              jquerylib_0.1.4          
[39] Rcpp_1.0.12               knitr_1.45               
[41] R.utils_2.12.3            httpuv_1.6.15            
[43] Matrix_1.6-5              tidyselect_1.2.1         
[45] rstudioapi_0.15.0         abind_1.4-5              
[47] yaml_2.3.8                viridis_0.6.4            
[49] codetools_0.2-19          processx_3.8.2           
[51] lattice_0.21-8            tibble_3.2.1             
[53] withr_3.0.0               evaluate_0.23            
[55] pillar_1.9.0              whisker_0.4.1            
[57] generics_0.1.3            rprojroot_2.0.4          
[59] RCurl_1.98-1.14           sparseMatrixStats_1.12.2 
[61] munsell_0.5.0             scales_1.3.0             
[63] glue_1.7.0                tools_4.3.1              
[65] BiocNeighbors_1.18.0      ScaledMatrix_1.8.1       
[67] locfit_1.5-9.9            fs_1.6.3                 
[69] rhdf5_2.44.0              grid_4.3.1               
[71] edgeR_3.42.4              colorspace_2.1-0         
[73] GenomeInfoDbData_1.2.10   beeswarm_0.4.0           
[75] BiocSingular_1.16.0       HDF5Array_1.28.1         
[77] vipor_0.4.7               cli_3.6.2                
[79] rsvd_1.0.5                fansi_1.0.6              
[81] viridisLite_0.4.2         S4Arrays_1.0.6           
[83] gtable_0.3.4              R.methodsS3_1.8.2        
[85] sass_0.4.9                digest_0.6.35            
[87] ggrepel_0.9.5             dqrng_0.3.2              
[89] farver_2.1.1              htmltools_0.5.6          
[91] R.oo_1.26.0               lifecycle_1.0.4          
[93] here_1.0.1                httr_1.4.7